The goal of this research program is to establish the basic mechanisms whereby mutations in thin filament proteins lead to complex human cardiomyopathies, and use this knowledge to create a predictive clinical tool. The ability to connect genotype to phenotype in patients with these mutations is limited because of the long time for disease to develop to the stage where it is diagnosed. A computational model of the cardiac thin filament developed in the last grant period, recently significantly extended to includ both actin and atomistic water, has allowed us to derive atomic level pictures of how specific mutation effect thin filament Ca2+ binding and ? -adrenergic signaling. These results have been fully verified by both in vitro and in vivo experiment. In this application we propose to transform our integrative approach from explanatory to predictive. For a set of thin filament mutations, we will use our computational model to obtain data in dynamics, structure, chemistry, and cooperativity that will be correlated to in vitro experimental results. These correlations will be used to create a set of rules that propose extension of the correlation to whole animal and eventually human disease state. We will then test these rules on a specific set of de novo mutations provided by our collaborators across the globe. Our prediction will be first validated in in vitro experiment and for a subset in newly generated transgenic mice. The methods used to study this system are highly innovative, and we also propose to extend our model for the first time to include ATP hydrolysis in myosin. This program will be implemented in the following 3 specific aims. Specific Aim 1: To utilize our recently developed all-atom, explicitly solvated model of the cardiac thin filament to evaluate and mechanistically define both the local and physically distant effects of known human mutations on complex structure and dynamics, and to map these results to in vitro experiment. Specific Aim 2: To test the predictive value of our rules developed from the integrative system (Aim 1) and to iteratively correct the rules. We will computationally query and stratify multiple unique disease-causing thin filament mutations into functionally defined groups. These predictions will be tested experimentally. Specific Aim 3. To extend the capability of the computational model to study myosin performing regulated chemistry on the thin filament.